The performance of the existing sparse Bayesian learning (SBL) methods for off-grid direction-of-arrival (DOA) estimation is dependent on the tradeoff between the accuracy and the computational workload. To speed up the off-grid SBL method while remain a reasonable accuracy, this letter describes a computationally efficient root SBL method for off-grid DOA estimation, which adopts a coarse grid and considers the sampled locations in the coarse grid as the adjustable parameters. We utilize an expectation-maximization algorithm to iteratively refine this coarse grid and illustrate that each updated grid point can be simply achieved by the root of a certain polynomial. Simulation results demonstrate that the computational complexity is significantly reduced, and the modeling error can be almost eliminated.

This fMRI study using MVPA reveals separated neural patterns for the first and second languages.

Written Chinese as a logographic system was developed over 3,000 y ago. Historically, Chinese children have learned to read by learning to associate the visuo-graphic properties of Chinese characters with lexical meaning, typically through handwriting. In recent years, however, many Chinese children have learned to use electronic communication devices based on the pinyin input method, which associates phonemes and English letters with characters. When children use pinyin to key in letters, their spelling no longer depends on reproducing the visuo-graphic properties of characters that are indispensable to Chinese reading, and, thus, typing in pinyin may conflict with the traditional learning processes for written Chinese. We therefore tested character reading ability and pinyin use by primary school children in three Chinese cites: Beijing ( n = 466), Guangzhou ( n = 477), and Jining ( n = 4,908). Children with severe reading difficulty are defined as those who were normal in nonverbal IQ but two grades (i.e., 2 y) behind in character-reading achievement. We found that the overall incidence rate of severe reading difficulty appears to be much higher than ever reported on Chinese reading. Crucially, we found that children’s reading scores were significantly negatively correlated with their use of the pinyin input method, suggesting that pinyin typing on e-devices hinders Chinese reading development. The Chinese language has survived the technological challenges of the digital era, but the benefits of communicating digitally may come with a cost in proficient learning of written Chinese.

Event-related potentials (ERP) are in general masked by various kinds of artifacts. To attenuate the effects of artifacts, various schemes have been introduced, such as epoch rejection, electro-oculogram (EOG) regression and independent component analysis (ICA). However, none of the existing techniques can automatically remove various kinds of artifacts from a single ERP epoch. EOG regression cannot handle artifacts other than ocular ones. ICA incorporating higher order statistics (HOS) normally requires data with large number of time samples in order that the solution is robust. In this paper we blindly separate the multi-channel ERP into source components by estimating the correlation matrices of the data. Since only second order statistics (SOS) is involved, the process performs well at the single epoch level. Automatic artifact identification is performed in the source domain by introducing objective criteria for various artifacts. Criteria are based on time domain signal amplitude for blink and spurious peak artifact, scalp distribution of signal power for eye movement artifact and power distribution of frequency components for muscle artifact. The correction procedure can be completed by removing the identified artifactual sources from the raw multi-channel ERP.

Motivation: Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue but cannot completely handle the second one. Here we develop a novel Fast Network Component Analysis (FastNCA) algorithm which has an analytical solution that is much faster and does not have the above limitations. Results: Firstly FastNCA is compared to NCA and NCA-r using synthetic data. The reconstruction of FastNCA is more accurate than that of NCA-r and comparable to that of properly converged NCA. FastNCA is not sensitive to the correlation among the input signals, while its performance does degrade a little but not as dramatically as that of NCA. Like NCA, FastNCA is not very sensitive to small inaccuracies in a priori information on the network topology. FastNCA is about several tens times faster than NCA and several hundreds times faster than NCA-r. Then, the method is applied to real yeast cell-cycle microarray data. The activities of the estimated cell-cycle regulators by FastNCA and NCA-r are compared to the semi-quantitative results obtained independently by Lee et al. (2002). It is shown here that there is a greater agreement between the results of FastNCA and Lee's, which is represented by the ratio 23/33, than that between the results of NCA-r and Lee's, which is 14/33. Availability: Software and supplementary materials are available from http://www.eee.hku.hk/~cqchang/FastNCA.htm Contact:cqchang@eee.hku.hk

The effect of mutual coupling between array elements is known to substantially degrade direction-of-arrival (DOA) estimation performance. Rank-reduction (RARE) methods can eliminate the mutual coupling effect without loss of array aperture. However, they might yield serious ambiguous DOA estimates in some scenarios. This letter proposes a modified RARE algorithm for DOA estimation, which overcomes the problem of ambiguous estimation by recursively building up RARE matrices. Moreover, due to the utilization of an additional unique structure of mutual coupling coefficients, the new method can give a better DOA estimation performance. Simulation results verify the efficiency of the proposed method.